Currently, the problems faced by this method mainly include the following points: 1) The time efficiency of training models, 2) Lack of model accuracy due to data expression and the approximation of some functions that are unfriendly to MPC, and 3) Security model that the framework can support, which is mainly defined by the Universal Composition Framework[1]}, [2]}, [3]}. The security model here includes the semi-honest and malicious adversary models (The semi-honest model requires the computing participants to strictly follow the protocol and only be curious about the data in the computation process, whereas the malicious adversary model does not restrict the participants to follow the protocol, they can deviate from the protocol and cause computation failure). The currently known two-party privacy-preserving neural network, like SecureML, only supports the semi-honest model, so our framework also follows it.
